Petro-physical property prediction

ABSTRACT

Embodiments relate to a method that includes measuring, by an electronic device at a surface of a wellbore, a functional characteristic of a drilling process. Embodiments may further include identifying, by the electronic device based on a change in the functional characteristic, a change in a petro-physical property of rock at a drill-bit while drilling. Embodiments may further include outputting, by the electronic device, an indication of the change in the petro-physical property of the rock at the drill-bit. Other embodiments may be described or claimed.

TECHNICAL FIELD

The present disclosure applies to petro-physical property prediction at a drill bit using machine learning.

BACKGROUND

Petro-physical properties such as resistivity, porosity, or density may be the basis for evaluation of hydrocarbon reservoirs. As an example, porosity may represent the void and empty volume inside of rock, and therefore its ability to store fluids. These properties are typically obtained from well logs or laboratory experiments on core plugs or drilled cuttings. Specifically, the data obtained from well logs may be based on downhole sensors such as logging while drilling (LWD) sensors. However, in many cases these sensors are located remotely from the drilling bit, and therefore may not provide an accurate measurement of the petro-physical properties at the drill bit.

SUMMARY

The present disclosure describes techniques that can be used for petro-physical property prediction at the drill bit using machine learning. Specifically, embodiments relate to the utilization of drilling surface parameters to predict formation petro-physical properties such as porosity at the drill-bit. Machine learning algorithms such as neural networks may be used to relate hydraulic and mechanical parameters of the drilling process as measured at the surface to the petro-physical properties of the rock at the drill-bit. As an advantageous result, it may be possible to determine a petro-physical property such as the porosity value of the rock at the drill-bit without the time delay that results from using a LWD sensor to identify the value after the drill-bit has passed the rock (a delay known as “depth-lag.”). Embodiments may therefore allow an operator to react early during geo-steering operations (e.g., changing a trajectory of the drill-bit) or mitigating a stuck-pipe situation. In some embodiments, the model may reduce or remove the need for LWD sensors, while in other embodiments the LWD sensors may be present and used to update the model based on values measured in the wellbore.

In some implementations, a computer-implemented method includes: measuring, by an electronic device at a surface of a wellbore, a functional characteristic of a drilling process; identifying, by the electronic device based on a change in the functional characteristic, a change in a petro-physical property of rock at a drill-bit while drilling; and outputting, by the electronic device, an indication of the change in the petro-physical property of the rock at the drill-bit.

The previously described implementation is implementable using a computer-implemented method; a non-transitory, computer-readable medium storing computer-readable instructions to perform the computer-implemented method; and a computer-implemented system including a computer memory interoperably coupled with a hardware processor configured to perform the computer-implemented method/the instructions stored on the non-transitory, computer-readable medium.

The subject matter described in this specification can be implemented in particular implementations to realize one or more of the following advantages. One such advantage is that it may be possible to accurately predict one or more petro-physical properties of rock at the drill-bit without the time-related depth-lag that can be caused by relying on one or more LWD sensors. As such, a stuck-pipe incident may be mitigated, or a change in drill trajectory may be performed. Another such advantage may be that embodiments may not require an LWD sensor, which may decrease the overall cost of the drilling process.

The details of one or more implementations of the subject matter of this specification are set forth in the Detailed Description, the accompanying drawings, and the claims. Other features, aspects, and advantages of the subject matter will become apparent from the Detailed Description, the claims, and the accompanying drawings.

DESCRIPTION OF DRAWINGS

FIG. 1 depicts an example drilling environment, in accordance with various embodiments.

FIG. 2 depicts an example technique for generation of a model that correlates functional characteristics of a drilling process with petro-physical property(s) of rock at the drill-bit while drilling, in accordance with various embodiments.

FIG. 3 depicts an example technique for identification of petro-physical property(s) of rock at the drill-bit while drilling, in accordance with various embodiments.

FIG. 4 depicts an example graphical user interface (GUI) associated with the techniques of FIG. 2 or 3, in accordance with various embodiments.

FIG. 5 is a block diagram illustrating an example computer system used to provide computational functionalities associated with described algorithms, methods, functions, processes, flows, and procedures as described in the present disclosure, according to some implementations of the present disclosure.

Like reference numbers and designations in the various drawings indicate like elements.

DETAILED DESCRIPTION

The following detailed description describes techniques for the identification of petro-physical properties of rock at a drill-bit, in accordance with various embodiments. Various modifications, alterations, and permutations of the disclosed implementations can be made and will be readily apparent to those of ordinary skill in the art, and the general principles defined may be applied to other implementations and applications, without departing from scope of the disclosure. In some instances, details unnecessary to obtain an understanding of the described subject matter may be omitted so as to not obscure one or more described implementations with unnecessary detail and inasmuch as such details are within the skill of one of ordinary skill in the art. The present disclosure is not intended to be limited to the described or illustrated implementations, but to be accorded the widest scope consistent with the described principles and features.

As previously noted, petro-physical properties such as resistivity, porosity, or density may be the basis for evaluation of hydrocarbon reservoirs. These properties are typically obtained from well logs produced by LWD sensors. However, in many cases these sensors are located remotely from the drilling bit, and therefore may not provide an accurate measurement of the petro-physical properties at the drill bit due to depth-lag.

However, the depth-lag may be responsible for challenges when geo-steering a well across a relatively thin layer of rock. Specifically, geo-steering of the drill-bit may be performed when the reservoir layer varies in depth or thickness, so the wellbore is targeted to stay in that zone. If the drill-bit exited the desired zone, then that exit would not be identified until the LWD sensors pass across the exit point, which may be significantly after the drill-bit passes that area. As a result, the exit from the zone may be too late to correct, or may require aggressive maneuvers such as sharp build or drop/dive to bring the wellbore back into the desired zone or layer of rock.

Additionally, it may be desirable to understand if the petro-physical properties of the rock at the drill-bit have changed in order to identify or mitigate a stuck-pipe incident. Specifically, a stuck-pipe incident may occur when the drill pipe or bottom hole assembly (BHA) becomes jammed, or becomes held against a side of the wellbore due to differential pressure forces. As used herein, a BHA may refer to a lowermost portion of a drilling structure which may include, for example, the drill-bit, one or more stabilizers, one or more collars, etc. The drill pipe may be a pipe that allows drilling fluid to be pumped into the well through the drill-bit. One reason the differential pressure may change may be due to a sudden increase in porosity in which the drilling fluids start leaking into the rock formation, which then pushes the drilling pipe and/or BHA towards the inner walls of the well and jamming it into place.

Based on the above examples of geo-steering or a stuck-pipe incident, it may be desirable to identify the petro-physical properties of the rock at the drill-bit rather than at a location further from the bit (e.g., at the LWD sensors.) Because, for engineering reasons, it may be difficult to move the LWD sensors closer to the bit, embodiments herein relate to machine learning (ML) and artificial intelligence (AI) techniques for relating the petro-physical properties of the rock to observable functional characteristics of the drilling process. As a result, functional characteristics of the drilling process may be observed at the surface of the wellbore, and those characteristics may be used to predict the petro-physical properties of the rock at the drill-bit. As one example, if the properties of the rock change during drilling such that geo-steering correction may be desired, then the change in properties may be observed and the trajectory of the drill-bit may be adjusted without the depth-lag that would result from waiting for the LWD sensors to arrive at the area. Similarly, if a property of the rock such as porosity increases at the drill-bit, then a possible stuck-pipe incident may be identified as occurring or about to occur, and mitigating actions may be taken.

In some embodiments, the model may be expanded to indicate the formation type of the rock at the drill-bit, such as whether the rock is carbonate, sandstone, or anhydrite. This formation type may provide valuable data for geo-steering to know if the drill-bit is in the desirable layer of the rock. This identification may be achieved by setting a threshold for minimum porosity values of the rock, or including the mineralogy type of the rock as an output during model building so that the mineralogy may be related to the surface drilling parameters.

In some embodiments, the LWD sensors may be used to verify or recalibrate the model. For example, the model may be used to predict petro-physical properties of the rock at the drill-bit. Subsequently, an LWD sensor may pass that portion of the rock and validate the actual petro-physical value, and compare the predicted value to the actual value. If the values match one another to within a tolerance (which may be either pre-defined or dynamic), then the model may not be updated. However, if the actual and predicted values do not match one another within the tolerance level, then the predicted values may be used to update the model. Updating the model may include, for example, replacing one or more of the values used in the model with the measured values, shifting one or more of the values of the model to a value between the previous value and the measured value, or some other form of updating the model.

In some embodiments, the functional characteristics of the drilling procedure may be or include one or more of the following:

rate of penetration (ROP): ROP relates to the speed at which the drill-bit is moving through the rock. The rate of penetration may be measured in units of, for example, feet per hour (ft/hr), although in other embodiments the rate of penetration may be measured in accordance with some other unit or metric;

torque: Torque relates to the amount of twisting force that is being applied to cause the drill-bit to spin;

revolutions per minute (RPM): RPM relates to the speed at which the drill-bit is spinning. It will be understood that the time span of “minute” is used herein for the sake of example only, and other embodiments may measure the speed of the drill-bit in accordance with some other time frame;

weight on bit (WOB): WOB is relates to the amount of force exerted by the drill-bit against the rock;

pumping rate: Pumping rate relates to the amount of hydraulic fluid being injected into the wellbore. The pumping rate may be measured in units of, for example, gallons per minute (GPM), although in other embodiments the pumping rate may be measured in accordance with some other unit or metric; and

stand pipe pressure (SPP): SPP relates to the total frictional pressure drop in the wellbore.

At a high level, the modelling may be based on the following. Initially, a model may be established. Establishment of the model may be based on previous well data (e.g., data acquired by LWD sensors in other wellbores.) In some embodiments, the previous well data may be based on test drillings in the location at which the wellbore is to be drilled. In other embodiments, the previous well data may be based on data acquired in offset wells that are in the same area as the wellbore that is to be drilled. In some embodiments, the model may be additionally or alternatively based on other data such as seismic reflection data or some other data. As previously noted, the model may be based on a ML algorithm such as a neural network or some other type of AI.

Specifically, the model may be used to correlate a petro-physical property of the rock at a given point with a functional characteristic of the drilling process. The petro-physical property may be one or more of the properties described above such as resistivity, porosity, density, etc. The functional characteristic may be one or more of the characteristics described above such as ROP, torque, RPM, WOB, pumping rate, SPP, or some other functional characteristic that may be observed or measured at the surface of the wellbore.

The drilling process may then begin and the petro-physical property of the rock at the drill bit may be predicted by the model. Specifically, the functional characteristics as described above may be monitored, and the model may be used to predict a petro-physical property of the rock at the drill-bit based on the functional characteristics. It will be understood that although a number of functional characteristics are described above, some embodiments may use all of the listed characteristics, a subset of the listed characteristics, or additional characteristics. Additionally, some embodiments may monitor for a single petro-physical property or a plurality of petro-physical properties.

If the model identifies that the drill-bit has exited the zone of interest (e.g., the rock layer in which the wellbore is desired), then the model may provide an indication to a user, automatically correct a trajectory of the drill-bit, or take some other remedial or mitigating action.

The model may identify that the drill-bit has exited the zone of interest based on a change in a petro-physical property of the rock such as a porosity value. Specifically, if the porosity value significantly decreases, or if the model identifies a change in formation lithology (e.g., the characteristics of the rock), the model may identify that the drill-bit has existed the zone of interest and take one or more of the actions described above.

Additionally or alternatively, if the models identifies a significant increase in the porosity value, then this increase may lead to a stuck-pipe incident as described above, especially if the event wasn't handled properly. In this situation, the model may display an alarm (e.g., a visual alarm, an audio alarm, or some other type of alarm) as an indication of possible stuck-pipe incident to the user. Thus, the user may take a mitigating action such as changing a functional characteristic of the drilling procedure (e.g., rotating faster, avoid stopping, or avoid making a drill pipe connection at that depth), altering a trajectory of the drill, or some other action which may mitigate the stuck-pipe situation.

In some embodiments, as described above, an LWD sensor may then pass by where the petro-physical property of the rock was predicted. The LWD sensor may take a measurement of the rock and compare the measured (e.g., actual) value against the predicted value as described above. In this embodiment, the model may then be updated if the actual value varies from the predicted value by an amount at or above a threshold (which may be pre-identified or dynamic).

FIG. 1 depicts an example drilling environment 100, in accordance with various embodiments. As may be seen, the drilling environment 100 may include a number of rock layers such as rock layers 115 and 117. A drilling BHA, which may include a drill-bit at 110 and LWD sensors 105, may be within a wellbore 120. In this embodiment, it may be desirable for the drilling BHA to be drilling through rock layer 115. However, as may be seen, the LWD sensors 105 may be far enough away from the drill-bit 110 that the drill may move from rock layer 115 to rock layer 117 before the LWD sensors 105 may be able to identify the change. For example, in some embodiments the LWD sensors 105 may be located approximately 100 feet from the drill-bit 110 (although in other embodiments the distance may be more or less).

However, as described above, in this embodiment the model may monitor one or more functional characteristics of the drilling procedure and identify, based on the functional characteristic(s), a change in the petro-physical property of the rock as the drill-bit 110 traverses from rock layer 115 to rock layer 117. As such, the model may take one or more remedial or mitigating actions such as changing a trajectory of the drill-bit 110 so that the drill-bit returns to rock layer 115, alerting an operator of the drill, or some other action.

FIG. 2 depicts an example technique for generation of a model that correlates functional characteristics of a drilling process with petro-physical property(s) of rock at the drill-bit while drilling, in accordance with various embodiments. For clarity of presentation, the description that follows generally describes the technique 200 in the context of the other Figures in this description. However, it will be understood that the technique 200 may be performed, for example, by any suitable system, environment, software, and hardware, or a combination of systems, environments, software, and hardware, as appropriate. In some implementations, various elements of the technique 200 may be run in parallel, in combination, in loops, or in any order.

The technique 200 may include identifying, at 202, one or more functional characteristics of the drilling process that are to be used for generation or use of the model. The functional characteristics may be one or more of the functional characteristics listed above. Selection of the functional characteristics may be based on, for example, the specific drilling procedure that is to be used, the drilling equipment, operator preference, etc. For example, certain drilling or measurement equipment may produce more reliable measurements for WOB than torque, and so torque may not be used for a given model.

The technique 200 may further include identifying, at 204, one or more petro-physical property(s) that are to be modeled. The petro-physical property(s) may be identified based on the type of rock, the type of action or incident that is to be monitored (e.g., geo-steering, stuck-pipe, or some other action/incident), etc.

The technique 200 may further include generating, at 206, a correlation model based on the functional characteristic(s) identified at 202 and the petro-physical property(s) identified at 204. As previously described, the generating of the correlation model may be based on a neural network or some other type of ML or AI. The generation of the correlation model may be based on previous well test data, data from other wells in the vicinity of the wellbore for which the correlation is desired, or other data such as seismic reflection data.

The technique 200 may further include predicting, at 208, the petro-physical property(s) at the drill bit. Specifically, the model may be used to monitor the one or more functional characteristics of the drilling processor that were identified at 202, and identify the one or more petro-physical properties from element 204.

FIG. 3 depicts an example technique 300 for identification of petro-physical property(s) of rock at the drill-bit of a drill, in accordance with various embodiments. Similarly to FIG. 2, and for clarity of presentation, the description that follows generally describes the technique 300 in the context of the other Figures in this description. However, it will be understood that the technique 300 may be performed, for example, by any suitable system, environment, software, and hardware, or a combination of systems, environments, software, and hardware, as appropriate. In some implementations, various elements of the technique 300 may be run in parallel, in combination, in loops, or in any order. Generally, the elements of technique 300 may be considered to at least partially overlap with the prediction of the petro-physical properties at 208.

The technique 300 may include measuring, at 302 at a surface of a wellbore, one or more functional characteristics of a drilling process. The one or more functional characteristics may be those described above (e.g., torque, RPM, etc.), a subset of the characteristics described above, or include additional functional characteristics of the drilling process. Measurement of the functional characteristics may be based on one or more of an accelerometer, a magnetometer, a pressure sensor, or some other sensor or that is attached to the drilling structure. For example, various sensors may be coupled with the drill shaft, one or more of the engines or motors that drive the drill shaft, an analog or digital output of a circuit controlling operation of the drill, or some other element or structure.

The technique 300 further includes identifying, at 304 based on a change in the functional characteristic, a change in a petro-physical property of the rock at a drill-bit while drilling. Specifically, the model may monitor one or more of the functional characteristics that are being measured at 302. A change in a functional characteristic may indicate a change in a petro-physical property of the rock such as a change in porosity, density, resistivity, etc.

The technique 300 further include outputting, at 306, an indication of the change in the petro-physical property of the rock at the drill-bit. For example, as described above the model may output an indication in the change via a GUI, an audio indication, or some other indication by which an operator, in charge of the drilling process, may identify the change. By identifying the change, the operator may be enabled to perform some remedial action such as changing a trajectory of the drill-bit, changing a drilling parameter to mitigate a stuck-pipe incident, or some other action. In some embodiments, the model (or the computing system on which the model is operating or with which the model is communicating) may be enabled to automatically perform one or more of the mitigating or remedial actions described above.

In some embodiments, the technique 300 may optionally further include measuring, at 308, the actual value of a petro-physical property of the rock. For example, as described above, a petro-physical property of the rock may be predicted at the drill-bit (e.g., as described with respect to element 208). Drilling may then continue to drill through the rock until one or more LWD sensors (e.g., LWD sensor 105) reaches the point at which the petro-physical property of the rock was predicted. The actual value of the petro-physical property of the rock may then be measured by the one or more sensors. If the actual value differs from the predicted value by an amount that is greater than a threshold, as described above, then the technique 300 may further optionally include updating, at 310, the model based on the actual value of the petro-physical property of the rock.

FIG. 4 depicts an example GUI associated with the techniques of FIG. 2 or 3, in accordance with various embodiments. The GUI 400 may include a number of elements as depicted in FIG. 4. However, it will be understood that the GUI 400 of FIG. 4 is intended as an example and other embodiments may include more or fewer elements, elements in different locations or sized differently, etc. Also, it will be understood that although the GUI 400 of FIG. 4 is generally depicted in terms of porosity, in other embodiments the GUI may relate to or address additional or alternative petro-physical properties of rock.

The GUI 400 may include a graph at 405 that indicates porosity values (along the X-axis as indicated in terms of percentage such that 0.1 is equivalent to 10%) and the depth of the well along the Y-axis (as measured in feet). It will be understood that the graph 405 is intended as an example graph and in other embodiments the units by which the X or Y-axes are measured may be different.

The graph 405 may include both calculated porosity values and actual porosity values. The GUI 400 may further depict a correlation coefficient at 420 which may indicate a correlation between the calculated porosity and the actual porosity, and thereby the strength of the predictive model. As shown in FIG. 4, the actual and calculated porosity values may generally overlap as may be seen at 415. At approximately 7000 feet, the actual values may stop being displayed, and so only the predicted values are depicted between approximately 7000 feet and 7200 feet.

The GUI 400 may further include an alternative graphical indication of the porosity value at 425. As shown, the graphical indication at 425 may be in terms of percentage and indicated by a dial that adjusts in real-time or semi-real-time such that an operator may monitor the porosity (or other petro-physical property) of the rock.

The GUI 400 may further present an indication at 430 of the type of rock at which the drill-bit is located. For example, the indication 430 in FIG. 4 indicates that the rock is limestone, while in other embodiments the indication 430 may indicate that the rock is carbonate, sandstone, anhydrite, or some other type of rock.

The GUI 400 may further include one or more alarms at 440. The alarms at 440 may be related to a stuck-pipe incident or a geo-steering event based on exiting a reservoir as shown. However, in other embodiments, the alarms may include different, more, or fewer alarm conditions. In some embodiments, the alarms at 440 may be based on a threshold condition at 435. For example, the threshold condition may indicate a change in porosity that may be used to evaluate a stuck-pipe or geo-steering condition. In the embodiment of FIG. 4, the operator has identified that the threshold condition at 435 is a 10% change in porosity; however, in other embodiments the threshold condition at 435 may be higher or lower. The specific threshold condition may be based on subjective criteria such as operator experience or previous experience with a well in the area, or an objective criterion such as the type of rock that is being drilled.

FIG. 5 is a block diagram of an example computer system 500 used to provide computational functionalities associated with described algorithms, methods, functions, processes, flows, and procedures described in the present disclosure, according to some implementations of the present disclosure. The illustrated computer 502 is intended to encompass any computing device such as a server, a desktop computer, a laptop/notebook computer, a wireless data port, a smart phone, a personal data assistant (PDA), a tablet computing device, or one or more processors within these devices, including physical instances, virtual instances, or both. The computer 502 can include input devices such as keypads, keyboards, and touch screens that can accept user information. Also, the computer 502 can include output devices that can convey information associated with the operation of the computer 502. The information can include digital data, visual data, audio information, or a combination of information. The information can be presented in a GUI.

The computer 502 can serve in a role as a client, a network component, a server, a database, a persistency, or components of a computer system for performing the subject matter described in the present disclosure. The illustrated computer 502 is communicably coupled with a network 530. In some implementations, one or more components of the computer 502 can be configured to operate within different environments, including cloud-computing-based environments, local environments, global environments, and combinations of environments.

At a top level, the computer 502 is an electronic computing device operable to receive, transmit, process, store, and manage data and information associated with the described subject matter. According to some implementations, the computer 502 can also include, or be communicably coupled with, an application server, an email server, a web server, a caching server, a streaming data server, or a combination of servers.

The computer 502 can receive requests over network 530 from a client application (for example, executing on another computer 502). The computer 502 can respond to the received requests by processing the received requests using software applications. Requests can also be sent to the computer 502 from internal users (for example, from a command console), external (or third) parties, automated applications, entities, individuals, systems, and computers.

Each of the components of the computer 502 can communicate using a system bus 503. In some implementations, any or all of the components of the computer 502, including hardware or software components, can interface with each other or the interface 504 (or a combination of both) over the system bus 503. Interfaces can use an application programming interface (API) 512, a service layer 513, or a combination of the API 512 and service layer 513. The API 512 can include specifications for routines, data structures, and object classes. The API 512 can be either computer-language independent or dependent. The API 512 can refer to a complete interface, a single function, or a set of APIs.

The service layer 513 can provide software services to the computer 502 and other components (whether illustrated or not) that are communicably coupled to the computer 502. The functionality of the computer 502 can be accessible for all service consumers using this service layer. Software services, such as those provided by the service layer 513, can provide reusable, defined functionalities through a defined interface. For example, the interface can be software written in JAVA, C++, or a language providing data in extensible markup language (XML) format. While illustrated as an integrated component of the computer 502, in alternative implementations, the API 512 or the service layer 513 can be stand-alone components in relation to other components of the computer 502 and other components communicably coupled to the computer 502. Moreover, any or all parts of the API 512 or the service layer 513 can be implemented as child or sub-modules of another software module, enterprise application, or hardware module without departing from the scope of the present disclosure.

The computer 502 includes an interface 504. Although illustrated as a single interface 504 in FIG. 5, two or more interfaces 504 can be used according to particular needs, desires, or particular implementations of the computer 502 and the described functionality. The interface 504 can be used by the computer 502 for communicating with other systems that are connected to the network 530 (whether illustrated or not) in a distributed environment. Generally, the interface 504 can include, or be implemented using, logic encoded in software or hardware (or a combination of software and hardware) operable to communicate with the network 530. More specifically, the interface 504 can include software supporting one or more communication protocols associated with communications. As such, the network 530 or the interface's hardware can be operable to communicate physical signals within and outside of the illustrated computer 502.

The computer 502 includes a processor 505. Although illustrated as a single processor 505 in FIG. 5, two or more processors 505 can be used according to particular needs, desires, or particular implementations of the computer 502 and the described functionality. Generally, the processor 505 can execute instructions and can manipulate data to perform the operations of the computer 502, including operations using algorithms, methods, functions, processes, flows, and procedures as described in the present disclosure.

The computer 502 also includes a database 506 that can hold data for the computer 502 and other components connected to the network 530 (whether illustrated or not). For example, database 506 can be an in-memory, conventional, or a database storing data consistent with the present disclosure. In some implementations, database 506 can be a combination of two or more different database types (for example, hybrid in-memory and conventional databases) according to particular needs, desires, or particular implementations of the computer 502 and the described functionality. Although illustrated as a single database 506 in FIG. 5, two or more databases (of the same, different, or combination of types) can be used according to particular needs, desires, or particular implementations of the computer 502 and the described functionality. While database 506 is illustrated as an internal component of the computer 502, in alternative implementations, database 506 can be external to the computer 502.

The computer 502 also includes a memory 507 that can hold data for the computer 502 or a combination of components connected to the network 530 (whether illustrated or not). Memory 507 can store any data consistent with the present disclosure. In some implementations, memory 507 can be a combination of two or more different types of memory (for example, a combination of semiconductor and magnetic storage) according to particular needs, desires, or particular implementations of the computer 502 and the described functionality. Although illustrated as a single memory 507 in FIG. 5, two or more memories 507 (of the same, different, or combination of types) can be used according to particular needs, desires, or particular implementations of the computer 502 and the described functionality. While memory 507 is illustrated as an internal component of the computer 502, in alternative implementations, memory 507 can be external to the computer 502.

The application 508 can be an algorithmic software engine providing functionality according to particular needs, desires, or particular implementations of the computer 502 and the described functionality. For example, application 508 can serve as one or more components, modules, or applications. Further, although illustrated as a single application 508, the application 508 can be implemented as multiple applications 508 on the computer 502. In addition, although illustrated as internal to the computer 502, in alternative implementations, the application 508 can be external to the computer 502.

The computer 502 can also include a power supply 514. The power supply 514 can include a rechargeable or non-rechargeable battery that can be configured to be either user- or non-user-replaceable. In some implementations, the power supply 514 can include power-conversion and management circuits, including recharging, standby, and power management functionalities.

In some implementations, the power supply 514 can include a power plug to allow the computer 502 to be plugged into a wall socket or a power source to, for example, power the computer 502 or recharge a rechargeable battery.

There can be any number of computers 502 associated with, or external to, a computer system containing computer 502, with each computer 502 communicating over network 530. Further, the terms “client,” “user,” and other appropriate terminology can be used interchangeably, as appropriate, without departing from the scope of the present disclosure. Moreover, the present disclosure contemplates that many users can use one computer 502 and one user can use multiple computers 502.

Described implementations of the subject matter can include one or more features, alone or in combination.

For example, in a first implementation, a computer-implemented method includes: measuring, by an electronic device at a surface of a wellbore, a functional characteristic of a drilling process while drilling; identifying, by the electronic device based on a change in the functional characteristic, a change in a petro-physical property of rock at a drill-bit while drilling; and outputting, by the electronic device, an indication of the change in the petro-physical property of the rock at the drill-bit.

The foregoing and other described implementations can each, optionally, include one or more of the following features:

A first feature, combinable with any of the following features, wherein the petro-physical property is porosity of the rock at the drill-bit while drilling.

A second feature, combinable with any of the following or previous features, further including identifying, by the electronic device based on an increase in the porosity, that a bottom hole assembly (BHA) is differentially jammed in the wellbore.

A third feature, combinable with any of the following or previous features, further including identifying, by the electronic device based on a decrease in the porosity, that a trajectory of the drill-bit is to be adjusted.

A fourth feature, combinable with any of the following or previous features, further including adjusting, by the electronic device based on the decrease in the porosity, the trajectory of the drill-bit.

A fifth feature, combinable with any of the following or previous features, wherein the petro-physical property is a resistivity of the rock or a density of the rock.

A sixth feature, combinable with any of the following or previous features, wherein the functional characteristic is a rate of penetration of the drill-bit, a torque on the drill-bit, a rotational speed of the drill-bit, a weight-on-bit measurement of the drill-bit, a pumping rate of the drilling process, or a stand pipe pressure of the wellbore.

A seventh feature, combinable with any of the following or previous features, wherein the identifying the change in the petro-physical property is based on a machine learning algorithm executed by the electronic device.

An eighth feature, combinable with any of the following or previous features, wherein the machine learning algorithm is a neural network.

A ninth feature, combinable with any of the following or previous features, wherein the machine learning algorithm is based on a relationship between a functional characteristic of a drilling process of a previously drilled wellbore and a petro-physical property of rock adjacent to the previously drilled wellbore.

A tenth feature, combinable with any of the following or previous features, further including updating, by the electronic device, the machine learning algorithm based on a sensor in the wellbore.

Another implementation includes one or more non-transitory computer-readable media including instructions that, upon execution of the instructions by one or more processors of an electronic device, are to cause the electronic device to: measure, at a surface of a wellbore, a functional characteristic of a drilling process while drilling; identify, based on a change in the functional characteristic, a change in a petro-physical property of rock at a drill-bit while drilling; and output an indication of the change in the petro-physical property of the rock at the drill-bit.

The foregoing and other described implementations can each, optionally, include one or more of the following features:

A first feature, combinable with any of the following features, wherein the petro-physical property is porosity of the rock at the drill-bit while drilling.

A second feature, combinable with any of the following or previous features, wherein the instructions are further to identify, based on an increase in the porosity, that a bottom hole assembly (BHA) is differentially jammed in the wellbore.

A third feature, combinable with any of the following or previous features, wherein the instructions are further to identify, based on a decrease in the porosity, that a trajectory of the drill-bit is to be adjusted.

A fourth feature, combinable with any of the following or previous features, wherein the instructions are further to adjust, based on the decrease in the porosity, the trajectory of the drill-bit.

A fifth feature, combinable with any of the following or previous features, wherein the petro-physical property is a resistivity of the rock or a density of the rock.

A sixth feature, combinable with any of the following or previous features, wherein the functional characteristic is a rate of penetration of the drill-bit, a torque on the drill-bit, a rotational speed of the drill-bit, a weight-on-bit measurement of the drill-bit, a pumping rate of the drilling process, or a stand pipe pressure of the wellbore.

A seventh feature, combinable with any of the following or previous features, wherein the instructions to identify the change in the petro-physical property include instructions to execute a machine learning algorithm to identify the change in the petro-physical property.

An eighth feature, combinable with any of the following or previous features, wherein the machine learning algorithm is a neural network.

A ninth feature, combinable with any of the following or previous features, wherein the machine learning algorithm is based on a relationship between a functional characteristic of a drilling process of a previously drilled wellbore and a petro-physical property of rock adjacent to the previously drilled wellbore.

A tenth feature, combinable with any of the following or previous features, wherein the instructions are further to update the machine learning algorithm based on a sensor in the wellbore.

In another implementation, an electronic device includes: a user interface (UI); one or more processors; and one or more non-transitory computer-readable media including instructions that, upon execution of the instructions by the one or more processors, are to cause the electronic device to: measure, at a surface of a wellbore, a functional characteristic of a drilling process; identify, based on a change in the functional characteristic, a change in a petro-physical property of rock at a drill-bit while drilling; and output, via the UI, an indication of the change in the petro-physical property of the rock at the drill-bit.

The foregoing and other described implementations can each, optionally, include one or more of the following features:

A first feature, combinable with any of the following features, wherein the petro-physical property is porosity of the rock at the drill-bit while drilling.

A second feature, combinable with any of the following or previous features, wherein the instructions are further to identify, based on an increase in the porosity, that a bottom hole assembly (BHA) is differentially jammed in the wellbore.

A third feature, combinable with any of the following or previous features, wherein the instructions are further to identify, based on a decrease in the porosity, that a trajectory of the drill-bit is to be adjusted.

A fourth feature, combinable with any of the following or previous features, wherein the instructions are further to adjust, based on the decrease in the porosity, the trajectory of the drill-bit.

A fifth feature, combinable with any of the following or previous features, wherein the petro-physical property is a resistivity of the rock or a density of the rock.

A sixth feature, combinable with any of the following or previous features, wherein the functional characteristic is a rate of penetration of the drill-bit, a torque on the drill-bit, a rotational speed of the drill-bit, a weight-on-bit measurement of the drill-bit, a pumping rate of the drilling process, or a stand pipe pressure of the wellbore.

A seventh feature, combinable with any of the following or previous features, wherein the instructions to identify the change in the petro-physical property include instructions to execute a machine learning algorithm to identify the change in the petro-physical property.

An eighth feature, combinable with any of the following or previous features, wherein the machine learning algorithm is a neural network.

A ninth feature, combinable with any of the following or previous features, wherein the machine learning algorithm is based on a relationship between a functional characteristic of a drilling process of a previously drilled wellbore and a petro-physical property of rock adjacent to the previously drilled wellbore.

A tenth feature, combinable with any of the following or previous features, wherein the instructions are further to update the machine learning algorithm based on a sensor in the wellbore.

Implementations of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, in tangibly embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Software implementations of the described subject matter can be implemented as one or more computer programs. Each computer program can include one or more modules of computer program instructions encoded on a tangible, non-transitory, computer-readable computer-storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively, or additionally, the program instructions can be encoded in/on an artificially generated propagated signal. For example, the signal can be a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to a suitable receiver apparatus for execution by a data processing apparatus. The computer-storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of computer-storage mediums.

The terms “data processing apparatus,” “computer,” and “electronic computer device” (or equivalent as understood by one of ordinary skill in the art) refer to data processing hardware. For example, a data processing apparatus can encompass all kinds of apparatuses, devices, and machines for processing data, including by way of example, a programmable processor, a computer, or multiple processors or computers. The apparatus can also include special purpose logic circuitry including, for example, a central processing unit (CPU), a field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). In some implementations, the data processing apparatus or special purpose logic circuitry (or a combination of the data processing apparatus or special purpose logic circuitry) can be hardware- or software-based (or a combination of both hardware- and software-based). The apparatus can optionally include code that creates an execution environment for computer programs, for example, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of execution environments. The present disclosure contemplates the use of data processing apparatuses with or without conventional operating systems, such as LINUX, UNIX, WINDOWS, MAC OS, ANDROID, or IOS.

A computer program, which can also be referred to or described as a program, software, a software application, a module, a software module, a script, or code, can be written in any form of programming language. Programming languages can include, for example, compiled languages, interpreted languages, declarative languages, or procedural languages. Programs can be deployed in any form, including as stand-alone programs, modules, components, subroutines, or units for use in a computing environment. A computer program can, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data, for example, one or more scripts stored in a markup language document, in a single file dedicated to the program in question, or in multiple coordinated files storing one or more modules, sub-programs, or portions of code. A computer program can be deployed for execution on one computer or on multiple computers that are located, for example, at one site or distributed across multiple sites that are interconnected by a communication network. While portions of the programs illustrated in the various Figures may be shown as individual modules that implement the various features and functionality through various objects, methods, or processes, the programs can instead include a number of sub-modules, third-party services, components, and libraries. Conversely, the features and functionality of various components can be combined into single components as appropriate. Thresholds used to make computational determinations can be statically, dynamically, or both statically and dynamically determined.

The methods, processes, or logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The methods, processes, or logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, for example, a CPU, an FPGA, or an ASIC.

Computers suitable for the execution of a computer program can be based on one or more of general and special purpose microprocessors and other kinds of CPUs. The elements of a computer are a CPU for performing or executing instructions and one or more memory devices for storing instructions and data. Generally, a CPU can receive instructions and data from (and write data to) a memory.

Graphics processing units (GPUs) can also be used in combination with CPUs. The GPUs can provide specialized processing that occurs in parallel to processing performed by CPUs. The specialized processing can include AI applications and processing, for example. GPUs can be used in GPU clusters or in multi-GPU computing.

A computer can include, or be operatively coupled to, one or more mass storage devices for storing data. In some implementations, a computer can receive data from, and transfer data to, the mass storage devices including, for example, magnetic, magneto-optical disks, or optical disks. Moreover, a computer can be embedded in another device, for example, a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive.

Computer-readable media (transitory or non-transitory, as appropriate) suitable for storing computer program instructions and data can include all forms of permanent/non-permanent and volatile/non-volatile memory, media, and memory devices. Computer-readable media can include, for example, semiconductor memory devices such as random access memory (RAM), read-only memory (ROM), phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices. Computer-readable media can also include, for example, magnetic devices such as tape, cartridges, cassettes, and internal/removable disks. Computer-readable media can also include magneto-optical disks and optical memory devices and technologies including, for example, digital video disc (DVD), CD-ROM, DVD+/−R, DVD-RAM, DVD-ROM, HD-DVD, and BLU-RAY. The memory can store various objects or data, including caches, classes, frameworks, applications, modules, backup data, jobs, web pages, web page templates, data structures, database tables, repositories, and dynamic information. Types of objects and data stored in-memory can include parameters, variables, algorithms, instructions, rules, constraints, and references. Additionally, the memory can include logs, policies, security or access data, and reporting files. The processor and the memory can be supplemented by, or incorporated into, special purpose logic circuitry.

Implementations of the subject matter described in the present disclosure can be implemented on a computer having a display device for providing interaction with a user, including displaying information to (and receiving input from) the user. Types of display devices can include, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), a light-emitting diode (LED), and a plasma monitor. Display devices can include a keyboard and pointing devices including, for example, a mouse, a trackball, or a trackpad. User input can also be provided to the computer through the use of a touch-screen, such as a tablet computer surface with pressure sensitivity or a multi-touch screen using capacitive or electric sensing. Other kinds of devices can be used to provide for interaction with a user, including to receive user feedback including, for example, sensory feedback including visual feedback, auditory feedback, or tactile feedback. Input from the user can be received in the form of acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to, and receiving documents from, a device that the user uses. For example, the computer can send web pages to a web browser on a user's client device in response to requests received from the web browser.

The term “GUI” can be used in the singular or the plural to describe one or more graphical user interfaces and each of the displays of a particular graphical user interface. Therefore, a GUI can represent any graphical user interface, including, but not limited to, a web browser, a touch-screen, or a command line interface (CLI) that processes information and efficiently presents the information results to the user. In general, a GUI can include a plurality of user interface (UI) elements, some or all associated with a web browser, such as interactive fields, pull-down lists, and buttons. These and other UI elements can be related to or represent the functions of the web browser.

Implementations of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, for example, as a data server, or that includes a middleware component, for example, an application server. Moreover, the computing system can include a front-end component, for example, a client computer having one or both of a graphical user interface or a web browser through which a user can interact with the computer. The components of the system can be interconnected by any form or medium of wireline or wireless digital data communication (or a combination of data communication) in a communication network. Examples of communication networks include a local area network (LAN), a radio access network (RAN), a metropolitan area network (MAN), a wide area network (WAN), Worldwide Interoperability for Microwave Access (WIMAX), a wireless local area network (WLAN) (for example, using 802.11 a/b/g/n or 802.20 or a combination of protocols), all or a portion of the Internet, or any other communication system or systems at one or more locations (or a combination of communication networks). The network can communicate with, for example, Internet Protocol (IP) packets, frame relay frames, asynchronous transfer mode (ATM) cells, voice, video, data, or a combination of communication types between network addresses.

The computing system can include clients and servers. A client and server can generally be remote from each other and can typically interact through a communication network. The relationship of client and server can arise by virtue of computer programs running on the respective computers and having a client-server relationship.

Cluster file systems can be any file system type accessible from multiple servers for read and update. Locking or consistency tracking may not be necessary since the locking of exchange file system can be done at application layer. Furthermore, Unicode data files can be different from non-Unicode data files.

While this specification contains many specific implementation details, these should not be construed as limitations on the scope of what may be claimed, but rather as descriptions of features that may be specific to particular implementations. Certain features that are described in this specification in the context of separate implementations can also be implemented, in combination, in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations, separately, or in any suitable sub-combination. Moreover, although previously described features may be described as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can, in some cases, be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.

Particular implementations of the subject matter have been described. Other implementations, alterations, and permutations of the described implementations are within the scope of the following claims as will be apparent to those skilled in the art. While operations are depicted in the drawings or claims in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed (some operations may be considered optional), to achieve desirable results. In certain circumstances, multitasking or parallel processing (or a combination of multitasking and parallel processing) may be advantageous and performed as deemed appropriate.

Moreover, the separation or integration of various system modules and components in the previously described implementations should not be understood as requiring such separation or integration in all implementations. It should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

Accordingly, the previously described example implementations do not define or constrain the present disclosure. Other changes, substitutions, and alterations are also possible without departing from the spirit and scope of the present disclosure.

Furthermore, any claimed implementation is considered to be applicable to at least a computer-implemented method; a non-transitory, computer-readable medium storing computer-readable instructions to perform the computer-implemented method; and a computer system including a computer memory interoperably coupled with a hardware processor configured to perform the computer-implemented method or the instructions stored on the non-transitory, computer-readable medium. 

What is claimed is:
 1. A method comprising: measuring, by an electronic device at a surface of a wellbore, a functional characteristic of a drilling process; identifying, by the electronic device based on a change in the functional characteristic, a change in a petro-physical property of rock at a drill-bit while drilling; and outputting, by the electronic device, an indication of the change in the petro-physical property of the rock at the drill-bit.
 2. The method of claim 1, wherein the petro-physical property is porosity of the rock at the drill-bit while drilling.
 3. The method of claim 1, wherein the petro-physical property is a resistivity of the rock or a density of the rock.
 4. The method of claim 1, further comprising identifying, by the electronic device based on the change in the petro-physical property of the rock at the drill-bit, that a bottom hole assembly (BHA) is differentially jammed in the wellbore.
 5. The method of claim 1, further comprising identifying, by the electronic device based on the change in the petro-physical property of the rock at the drill-bit, that a trajectory of the drill-bit is to be adjusted.
 6. The method of claim 1, wherein the functional characteristic is a rate of penetration of the drill-bit, a torque on the drill-bit, a rotational speed of the drill-bit, a weight-on-bit measurement of the drill-bit, a pumping rate of the drilling process, or a stand pipe pressure of the wellbore.
 7. The method of claim 1, wherein the identifying the change in the petro-physical property is based on a machine learning algorithm executed by the electronic device.
 8. One or more non-transitory computer-readable media comprising instructions that, upon execution of the instructions by one or more processors of an electronic device, are to cause the electronic device to: measure, at a surface of a wellbore, a functional characteristic of a drilling process; identify, based on a change in the functional characteristic, a change in porosity of the rock at the drill-bit while drilling; and output an indication of the change in the porosity of the rock at the drill-bit.
 9. The one or more non-transitory computer-readable media of claim 8, wherein the instructions are further to identify, based on an increase in the porosity, that a bottom hole assembly (BHA) is differentially jammed in the wellbore.
 10. The one or more non-transitory computer-readable media of claim 8, wherein the instructions are further to identify, based on a decrease in the porosity, that a trajectory of the drill-bit is to be adjusted.
 11. The one or more non-transitory computer-readable media of claim 10, wherein the instructions are further to adjust, based on the decrease in the porosity, the trajectory of the drill-bit.
 12. The one or more non-transitory computer-readable media of claim 8, wherein the functional characteristic is a rate of penetration of the drill-bit, a torque on the drill-bit, a rotational speed of the drill-bit, a weight-on-bit measurement of the drill-bit, a pumping rate of the drilling process, or a stand pipe pressure of the wellbore.
 13. The one or more non-transitory computer-readable media of claim 8, wherein the instructions to identify the change in the porosity include instructions to execute a machine learning algorithm to identify the change in the porosity.
 14. An electronic device comprising: a user interface (UI); one or more processors; and one or more non-transitory computer-readable media including instructions that, upon execution of the instructions by the one or more processors, are to cause the electronic device to: measure, at a surface of a wellbore, a functional characteristic of a drilling process; execute a machine-learning algorithm to identify, based on a change in the functional characteristic, a change in a petro-physical property of rock at a drill-bit while drilling; and output, via the UI, an indication of the change in the petro-physical property of the rock at the drill-bit.
 15. The electronic device of claim 14, wherein the petro-physical property is porosity of the rock at the drill-bit while drilling.
 16. The electronic device of claim 14, wherein the petro-physical property is a resistivity of the rock or a density of the rock.
 17. The electronic device of claim 14, wherein the functional characteristic is a rate of penetration of the drill-bit, a torque on the drill-bit, a rotational speed of the drill-bit, a weight-on-bit measurement of the drill-bit, a pumping rate of the drilling process, or a stand pipe pressure of the wellbore.
 18. The electronic device of claim 14, wherein the machine learning algorithm is a neural network.
 19. The electronic device of claim 14, wherein the machine learning algorithm is based on a relationship between a functional characteristic of a drilling process of a previously drilled wellbore and a petro-physical property of rock adjacent to the previously drilled wellbore.
 20. The electronic device of claim 14, wherein the instructions are further to update the machine learning algorithm based on a sensor in the wellbore. 